Created src/prompt_constants.py (550+ lines) porting all constants from npm src/constants/:
┌──────────────────┬───────────────────────────┬───────────────────────────────────────────────┐ │ Category │ npm Source │ Items │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ Product metadata │ product.ts │ URLs, base URLs │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ System prefixes │ system.ts │ 3 prompt prefixes │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ Cyber risk │ cyberRiskInstruction.ts │ Safety instruction │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ API limits │ apiLimits.ts │ 10 image/PDF/media limits │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ Tool limits │ toolLimits.ts │ 6 result size constants │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ Spinner verbs │ spinnerVerbs.ts │ 187 whimsical gerunds │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ Completion verbs │ turnCompletionVerbs.ts │ 8 past-tense verbs │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ Figures/symbols │ figures.ts │ 25 Unicode UI symbols │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ XML tags │ xml.ts │ 30+ tag constants │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ Messages │ messages.ts │ NO_CONTENT_MESSAGE │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ Date utilities │ common.ts │ 4 functions │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ Section caching │ systemPromptSections.ts │ Memoized/volatile sections │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ Output styles │ outputStyles.ts │ 3 built-in configs │ ├──────────────────┼───────────────────────────┼───────────────────────────────────────────────┤ │ Prompt helpers │ prompts.ts │ Knowledge cutoff, language, scratchpad, hooks │ └──────────────────┴───────────────────────────┴───────────────────────────────────────────────┘ 91 new tests in tests/test_prompt_constants.py. All 17 SQL todos done.
This commit is contained in:
Abdelrahman Abdallah
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"""Conversation compaction service.
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Mirrors the npm ``src/services/compact/compact.ts`` and
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``src/services/compact/prompt.ts`` modules. Provides:
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- The 9-section summarisation prompt (``get_compact_prompt``).
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- XML-tag formatting/stripping (``format_compact_summary``).
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- The post-compact user summary message builder
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(``get_compact_user_summary_message``).
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- The core ``compact_conversation`` entry point that an
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``/compact`` slash command or auto-compact subsystem can call.
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"""
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from __future__ import annotations
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import re
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from dataclasses import dataclass, field
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from typing import TYPE_CHECKING, Any
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from .agent_context_usage import estimate_tokens
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from .agent_session import AgentMessage
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if TYPE_CHECKING:
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from .agent_runtime import LocalCodingAgent
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# ---------------------------------------------------------------------------
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# Constants
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# ---------------------------------------------------------------------------
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AUTOCOMPACT_BUFFER_TOKENS = 13_000
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"""How many tokens to reserve below the effective context window before
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auto-compact fires (same as the npm ``AUTOCOMPACT_BUFFER_TOKENS``)."""
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ERROR_NOT_ENOUGH_MESSAGES = 'Not enough messages to compact.'
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ERROR_INCOMPLETE_RESPONSE = (
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'The summary response was incomplete. '
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'The conversation was not compacted.'
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)
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ERROR_USER_ABORT = 'Compaction canceled.'
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MAX_COMPACT_FAILURES = 3
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"""Circuit-breaker – stop retrying auto-compact after this many consecutive
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failures (mirrors the npm implementation)."""
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# ---------------------------------------------------------------------------
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# Prompt construction (npm ``src/services/compact/prompt.ts``)
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# ---------------------------------------------------------------------------
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_NO_TOOLS_PREAMBLE = """\
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CRITICAL: Respond with TEXT ONLY. Do NOT call any tools.
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- Do NOT use Read, Bash, Grep, Glob, Edit, Write, or ANY other tool.
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- You already have all the context you need in the conversation above.
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- Tool calls will be REJECTED and will waste your only turn — you will fail the task.
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- Your entire response must be plain text: an <analysis> block followed by a <summary> block.
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"""
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_DETAILED_ANALYSIS_INSTRUCTION = """\
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Before providing your final summary, wrap your analysis in <analysis> tags to \
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organize your thoughts and ensure you've covered all necessary points. In your \
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analysis process:
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1. Chronologically analyze each message and section of the conversation. \
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For each section thoroughly identify:
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- The user's explicit requests and intents
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- Your approach to addressing the user's requests
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- Key decisions, technical concepts and code patterns
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- Specific details like:
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- file names
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- full code snippets
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- function signatures
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- file edits
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- Errors that you ran into and how you fixed them
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- Pay special attention to specific user feedback that you received, \
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especially if the user told you to do something differently.
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2. Double-check for technical accuracy and completeness, addressing each \
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required element thoroughly."""
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_BASE_COMPACT_PROMPT = f"""\
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Your task is to create a detailed summary of the conversation so far, paying \
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close attention to the user's explicit requests and your previous actions.
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This summary should be thorough in capturing technical details, code patterns, \
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and architectural decisions that would be essential for continuing development \
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work without losing context.
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{_DETAILED_ANALYSIS_INSTRUCTION}
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Your summary should include the following sections:
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1. Primary Request and Intent: Capture all of the user's explicit requests \
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and intents in detail
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2. Key Technical Concepts: List all important technical concepts, technologies, \
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and frameworks discussed.
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3. Files and Code Sections: Enumerate specific files and code sections examined, \
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modified, or created. Pay special attention to the most recent messages and \
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include full code snippets where applicable and include a summary of why this \
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file read or edit is important.
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4. Errors and fixes: List all errors that you ran into, and how you fixed them. \
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Pay special attention to specific user feedback that you received, especially if \
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the user told you to do something differently.
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5. Problem Solving: Document problems solved and any ongoing troubleshooting \
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efforts.
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6. All user messages: List ALL user messages that are not tool results. These \
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are critical for understanding the users' feedback and changing intent.
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7. Pending Tasks: Outline any pending tasks that you have explicitly been asked \
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to work on.
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8. Current Work: Describe in detail precisely what was being worked on \
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immediately before this summary request, paying special attention to the most \
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recent messages from both user and assistant. Include file names and code \
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snippets where applicable.
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9. Optional Next Step: List the next step that you will take that is related to \
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the most recent work you were doing. IMPORTANT: ensure that this step is \
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DIRECTLY in line with the user's most recent explicit requests, and the task \
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you were working on immediately before this summary request. If your last task \
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was concluded, then only list next steps if they are explicitly in line with the \
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users request. Do not start on tangential requests or really old requests that \
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were already completed without confirming with the user first.
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If there is a next step, include direct quotes from the \
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most recent conversation showing exactly what task you were working on and where \
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you left off. This should be verbatim to ensure there's no drift in task \
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interpretation.
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Here's an example of how your output should be structured:
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<example>
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<analysis>
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[Your thought process, ensuring all points are covered thoroughly and accurately]
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</analysis>
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<summary>
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1. Primary Request and Intent:
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[Detailed description]
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2. Key Technical Concepts:
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- [Concept 1]
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- [Concept 2]
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- [...]
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3. Files and Code Sections:
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- [File Name 1]
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- [Summary of why this file is important]
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- [Summary of the changes made to this file, if any]
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- [Important Code Snippet]
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- [File Name 2]
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- [Important Code Snippet]
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- [...]
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4. Errors and fixes:
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- [Detailed description of error 1]:
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- [How you fixed the error]
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- [User feedback on the error if any]
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- [...]
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5. Problem Solving:
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[Description of solved problems and ongoing troubleshooting]
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6. All user messages:
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- [Detailed non tool use user message]
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- [...]
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7. Pending Tasks:
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- [Task 1]
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- [Task 2]
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- [...]
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8. Current Work:
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[Precise description of current work]
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9. Optional Next Step:
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[Optional Next step to take]
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</summary>
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</example>
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Please provide your summary based on the conversation so far, following this \
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structure and ensuring precision and thoroughness in your response.
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There may be additional summarization instructions provided in the included \
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context. If so, remember to follow these instructions when creating the above \
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summary. Examples of instructions include:
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<example>
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## Compact Instructions
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When summarizing the conversation focus on typescript code changes and also \
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remember the mistakes you made and how you fixed them.
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</example>
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<example>
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# Summary instructions
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When you are using compact - please focus on test output and code changes. \
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Include file reads verbatim.
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</example>
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"""
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_NO_TOOLS_TRAILER = (
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'\n\nREMINDER: Do NOT call any tools. Respond with plain text only — '
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'an <analysis> block followed by a <summary> block. '
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'Tool calls will be rejected and you will fail the task.'
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)
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def get_compact_prompt(custom_instructions: str | None = None) -> str:
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"""Build the full compact prompt, optionally appending user instructions."""
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prompt = _NO_TOOLS_PREAMBLE + _BASE_COMPACT_PROMPT
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if custom_instructions and custom_instructions.strip():
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prompt += f'\n\nAdditional Instructions:\n{custom_instructions}'
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prompt += _NO_TOOLS_TRAILER
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return prompt
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# ---------------------------------------------------------------------------
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# Summary formatting
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# ---------------------------------------------------------------------------
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def format_compact_summary(summary: str) -> str:
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"""Strip the ``<analysis>`` scratchpad and unwrap ``<summary>`` tags.
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Mirrors the npm ``formatCompactSummary`` helper.
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"""
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formatted = re.sub(r'<analysis>[\s\S]*?</analysis>', '', summary)
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match = re.search(r'<summary>([\s\S]*?)</summary>', formatted)
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if match:
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content = match.group(1).strip()
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formatted = re.sub(
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r'<summary>[\s\S]*?</summary>',
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f'Summary:\n{content}',
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formatted,
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)
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# Collapse runs of blank lines.
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formatted = re.sub(r'\n\n+', '\n\n', formatted)
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return formatted.strip()
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def get_compact_user_summary_message(
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summary: str,
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*,
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suppress_follow_up: bool = False,
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transcript_path: str | None = None,
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) -> str:
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"""Build the user-facing summary that replaces compacted messages.
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Mirrors the npm ``getCompactUserSummaryMessage`` helper.
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"""
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formatted = format_compact_summary(summary)
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base = (
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'This session is being continued from a previous conversation that '
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'ran out of context. The summary below covers the earlier portion '
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f'of the conversation.\n\n{formatted}'
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)
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if transcript_path:
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base += (
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'\n\nIf you need specific details from before compaction '
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'(like exact code snippets, error messages, or content you '
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'generated), read the full transcript at: '
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f'{transcript_path}'
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)
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if suppress_follow_up:
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base += (
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'\nContinue the conversation from where it left off without '
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'asking the user any further questions. Resume directly — do '
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'not acknowledge the summary, do not recap what was happening, '
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'do not preface with "I\'ll continue" or similar. Pick up the '
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'last task as if the break never happened.'
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)
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return base
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# ---------------------------------------------------------------------------
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# Compaction result
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# ---------------------------------------------------------------------------
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@dataclass
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class CompactionResult:
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"""Outcome of a ``compact_conversation`` call."""
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boundary_message: AgentMessage
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summary_messages: list[AgentMessage] = field(default_factory=list)
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messages_to_keep: list[AgentMessage] = field(default_factory=list)
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pre_compact_token_count: int = 0
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post_compact_token_count: int = 0
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summary_text: str = ''
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error: str | None = None
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# ---------------------------------------------------------------------------
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# Core compaction logic
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# ---------------------------------------------------------------------------
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def compact_conversation(
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agent: 'LocalCodingAgent',
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custom_instructions: str | None = None,
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) -> CompactionResult:
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"""Perform an LLM-backed conversation compaction.
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1. Build the compact prompt (9-section template).
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2. Collect the session messages to summarise.
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3. Send them + the compact prompt to the model.
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4. Parse ``<summary>`` from the response.
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5. Replace session messages with:
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boundary marker → summary user message → preserved tail.
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Returns a :class:`CompactionResult` with diagnostics.
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"""
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session = agent.last_session
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if session is None or len(session.messages) == 0:
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return CompactionResult(
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boundary_message=_build_boundary('No session to compact.'),
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error=ERROR_NOT_ENOUGH_MESSAGES,
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)
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# ---- Determine which messages to compact vs preserve ----
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# We keep the most recent ``preserve_count`` messages untouched.
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preserve_count = max(
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getattr(agent.runtime_config, 'compact_preserve_messages', 4), 1
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)
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# Identify the prefix count (system-injected messages that precede the
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# real conversation, e.g. a compaction-replay boundary).
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prefix_count = 0
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for msg in session.messages:
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if msg.metadata.get('kind') == 'compact_boundary':
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prefix_count += 1
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else:
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break
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total = len(session.messages)
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tail_count = min(preserve_count, max(total - prefix_count, 0))
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compact_end = total - tail_count
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if compact_end <= prefix_count:
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return CompactionResult(
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boundary_message=_build_boundary('Not enough messages after prefix.'),
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error=ERROR_NOT_ENOUGH_MESSAGES,
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)
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candidates = session.messages[prefix_count:compact_end]
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preserved_tail = list(session.messages[compact_end:])
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if not candidates:
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return CompactionResult(
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boundary_message=_build_boundary('Nothing to compact.'),
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error=ERROR_NOT_ENOUGH_MESSAGES,
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)
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# ---- Estimate pre-compact token count ----
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model = agent.model_config.model
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pre_tokens = sum(estimate_tokens(m.content, model) for m in session.messages)
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# ---- Build the compact request messages ----
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compact_prompt = get_compact_prompt(custom_instructions)
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# We send the system prompt + candidate messages + the compact prompt as
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# a user message. The model returns the summary.
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api_messages: list[dict[str, Any]] = []
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# System prompt (from session)
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for part in session.system_prompt_parts:
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if part.strip():
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api_messages.append({'role': 'system', 'content': part})
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# Candidate messages (the ones to be summarised)
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for msg in candidates:
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api_messages.append(msg.to_openai_message())
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# The compact prompt as the final user turn
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api_messages.append({'role': 'user', 'content': compact_prompt})
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# ---- Call the model ----
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try:
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turn = agent.client.complete(api_messages, tools=[])
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except Exception as exc:
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return CompactionResult(
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boundary_message=_build_boundary(f'Compact API call failed: {exc}'),
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error=str(exc),
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)
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raw_summary = turn.content or ''
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if not raw_summary.strip():
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return CompactionResult(
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boundary_message=_build_boundary('Model returned empty summary.'),
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error=ERROR_INCOMPLETE_RESPONSE,
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)
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# ---- Format the summary ----
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summary_text = format_compact_summary(raw_summary)
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user_summary_content = get_compact_user_summary_message(raw_summary)
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# ---- Build post-compact messages ----
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boundary = _build_boundary(
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f'Earlier conversation ({len(candidates)} messages, ~{pre_tokens} tokens) '
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f'was compacted.',
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)
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summary_msg = AgentMessage(
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role='user',
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content=user_summary_content,
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message_id='compact_summary',
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metadata={'kind': 'compact_summary', 'is_compact_summary': True},
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)
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# Replace session messages in-place
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session.messages = (
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session.messages[:prefix_count]
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+ [boundary, summary_msg]
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+ preserved_tail
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)
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# ---- Post-compact token estimate ----
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post_tokens = sum(estimate_tokens(m.content, model) for m in session.messages)
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return CompactionResult(
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boundary_message=boundary,
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summary_messages=[summary_msg],
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messages_to_keep=preserved_tail,
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pre_compact_token_count=pre_tokens,
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post_compact_token_count=post_tokens,
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summary_text=summary_text,
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)
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _build_boundary(note: str) -> AgentMessage:
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"""Create a compact-boundary system message."""
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return AgentMessage(
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role='user',
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content=f'<system-reminder>\n{note}\n</system-reminder>',
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message_id='compact_boundary',
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metadata={'kind': 'compact_boundary'},
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)
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Reference in New Issue
Block a user